GMS 6719: Fundamentals of Computational Neuroscience

Spring 2010

 

NEW – This course will be offered via distance learning. Please contact the instructor if you are interested.

 

Class Meeting: T, Th 1:55-2:45, UFBI L1-101

Class Homepage: http://nrg.mbi.ufl.edu/courses/FCN/fcn_index.html

 

Instructor: Justin C. Sanchez, Ph.D. (http://nrg.mbi.ufl.edu)

Office hours: TBD

 

Prerequisite: This course is open to all graduate students with an interest in Systems Neurophysiology, Neural Computation, Neural Engineering, and Experimental Neurophysiological Analysis. Only a basic knowledge of calculus and computing is required.

  

Required textbook: Fundamentals of Computational Neuroscience, Thomas P. Trappenberg, Oxford University Press. 2002. ISBN: 0-19-851582-0

 

Course Objectives: This course will present the major concepts of neural signaling and communication from the single neuron to systems of neural ensembles. We will discuss the role of neural computation for advancing knowledge of information-processing in the brain. It will be shown how experimental data can be summarized and predicted through computational modeling. Whenever possible, computer simulations will be used to provide real examples for student experimentation.

 

Grade Determination: 1/3 Homework, 1/3 midterm, 1/3 Final

 

Policies: Late policy for homeworks: 20% deducted per day, unless prior arrangements were made with the instructor. Students are encouraged to work together on the homework, but the work that is handed in must be individual work.

 

Schedule

 

Week 1.

 

            Lecture 1

Chapter 1. Introduction

á       Origins

á       What is a model?

 

 

Week 2

 

Lecture 2

Chapter 2. Neurons and conductance-based models

á       Basic synaptic mechanisms

á       Generation of action potentials: Hodgkin-Huxley

á       Dendritic trees and the propagation of action potentials 

 

 

Week 3.

 

Lecture 3

     Chapter 3. Spiking neurons and response variability

á       Integrate and fire

á       The spike-response model

á       Spike time variability

Lecture 4

     Chapter 4a. Neurons in a network

á      Organizations of neuronal networks

 

 

Week 4.

 

Lecture 5

     Chapter 4b. Neurons in a network

á       Information transmission in networks

á       Population dynamics

Lecture 6

     Chapter 5a. Representations and the neural code

á       How neurons communicate

á       Neural coding

á       Information theory

 

 

Week 5.

 

Lecture 7

     Chapter 5b. Representations and the neural code

á       Population coding and decoding

á       Distributed representation

 

Midterm 

 

 

Week 6.

 

Lecture 8

     Chapter 6a. Feed-forward mapping networks

á       Perception, function representation, and look-up tables

á       Multilayer mapping networks

Lecture 9

Chapter 6b. Feed-forward mapping networks

á       Learning, generalization, and biological interpretations

á      Biological interpretations

 

 

Week 7.

 

Lecture 10

     Chapter 7. Associators and synaptic plasticity

á       Associative memory and Hebbian learning

á       The temporal structure of Hebbian plasticity: LTP and LTD

Lecture 11

     Chapter 8. Auto-associative memory and network dynamics

á       Recurrent memory

á       Comparisons with hippocampus

 

 

Week 8.

 

Lecture 12

á       Memory capacity

á       Dynamical Systems Intro

 

 

Week 9.

 

Lecture 13

     Chapter 10a. Supervised learning and rewards systems

á       Supervised learning in motor systems

Lecture 14

Chapter 10b. Supervised learning and rewards systems

á       Neural mechanisms in supervised learning

á       Reward Learning

Chapter 11a. System level organization

á       Large scale anatomical and functional organization

á       Modular mapping

 

 

Week 10.

 

Lecture 15

Chapter 11b. System level organization

á       Putting it all together (neurobiology, computation, modeling, systems theory, learning)

á       Brain-Machine Interfaces

Final Exam

 

 

 

Academic Honesty

As a result of completing the registration form at the University of Florida, every student has signed the following statement: "I understand that the University of Florida expects its students to be honest in all their academic work. I agree to adhere to this commitment to academic honesty and understand that my failure to comply with this commitment may result in disciplinary action up to and including expulsion from the University." We agree to comply with the new Honor Code, which specifies that "We, the members of the University of Florida community, pledge to hold ourselves and our peers to the highest standards of honesty and integrity.